COCO Image Viewer¶
This notebook will allow you to view details about a COCO dataset and preview segmentations on annotated images. Learn more about it at: http://cocodataset.org/
In [1]:
import IPython
import os
import json
import random
import numpy as np
import requests
from io import BytesIO
import base64
from math import trunc
from PIL import Image as PILImage
from PIL import ImageDraw as PILImageDraw
Image Viewer Logic¶
In [2]:
# Load the dataset json
class CocoDataset():
def __init__(self, annotation_path, image_dir):
self.annotation_path = annotation_path
self.image_dir = image_dir
self.colors = colors = ['blue', 'purple', 'red', 'green', 'orange', 'salmon', 'pink', 'gold',
'orchid', 'slateblue', 'limegreen', 'seagreen', 'darkgreen', 'olive',
'teal', 'aquamarine', 'steelblue', 'powderblue', 'dodgerblue', 'navy',
'magenta', 'sienna', 'maroon']
json_file = open(self.annotation_path)
self.coco = json.load(json_file)
json_file.close()
self.process_info()
self.process_licenses()
self.process_categories()
self.process_images()
self.process_segmentations()
def display_info(self):
print('Dataset Info:')
print('=============')
for key, item in self.info.items():
print(' {}: {}'.format(key, item))
requirements = [['description', str],
['url', str],
['version', str],
['year', int],
['contributor', str],
['date_created', str]]
for req, req_type in requirements:
if req not in self.info:
print('ERROR: {} is missing'.format(req))
elif type(self.info[req]) != req_type:
print('ERROR: {} should be type {}'.format(req, str(req_type)))
print('')
def display_licenses(self):
print('Licenses:')
print('=========')
requirements = [['id', int],
['url', str],
['name', str]]
for license in self.licenses:
for key, item in license.items():
print(' {}: {}'.format(key, item))
for req, req_type in requirements:
if req not in license:
print('ERROR: {} is missing'.format(req))
elif type(license[req]) != req_type:
print('ERROR: {} should be type {}'.format(req, str(req_type)))
print('')
print('')
def display_categories(self):
print('Categories:')
print('=========')
for sc_key, sc_val in self.super_categories.items():
print(' super_category: {}'.format(sc_key))
for cat_id in sc_val:
print(' id {}: {}'.format(cat_id, self.categories[cat_id]['name']))
print('')
def display_image(self, image_id, show_polys=True, show_bbox=True, show_crowds=True, use_url=False):
print('Image:')
print('======')
if image_id == 'random':
image_id = random.choice(list(self.images.keys()))
# Print the image info
image = self.images[image_id]
for key, val in image.items():
print(' {}: {}'.format(key, val))
# Open the image
if use_url:
image_path = image['coco_url']
response = requests.get(image_path)
image = PILImage.open(BytesIO(response.content))
else:
image_path = os.path.join(self.image_dir, image['file_name'])
image = PILImage.open(image_path)
buffer = BytesIO()
image.save(buffer, format='PNG')
buffer.seek(0)
data_uri = base64.b64encode(buffer.read()).decode('ascii')
image_path = "data:image/png;base64,{0}".format(data_uri)
# Calculate the size and adjusted display size
max_width = 600
image_width, image_height = image.size
adjusted_width = min(image_width, max_width)
adjusted_ratio = adjusted_width / image_width
adjusted_height = adjusted_ratio * image_height
# Create list of polygons to be drawn
polygons = {}
bbox_polygons = {}
rle_regions = {}
poly_colors = {}
print(' segmentations ({}):'.format(len(self.segmentations[image_id])))
for i, segm in enumerate(self.segmentations[image_id]):
polygons_list = []
if segm['iscrowd'] != 0:
# Gotta decode the RLE
px = 0
x, y = 0, 0
rle_list = []
for j, counts in enumerate(segm['segmentation']['counts']):
if j % 2 == 0:
# Empty pixels
px += counts
else:
# Need to draw on these pixels, since we are drawing in vector form,
# we need to draw horizontal lines on the image
x_start = trunc(trunc(px / image_height) * adjusted_ratio)
y_start = trunc(px % image_height * adjusted_ratio)
px += counts
x_end = trunc(trunc(px / image_height) * adjusted_ratio)
y_end = trunc(px % image_height * adjusted_ratio)
if x_end == x_start:
# This is only on one line
rle_list.append({'x': x_start, 'y': y_start, 'width': 1 , 'height': (y_end - y_start)})
if x_end > x_start:
# This spans more than one line
# Insert top line first
rle_list.append({'x': x_start, 'y': y_start, 'width': 1, 'height': (image_height - y_start)})
# Insert middle lines if needed
lines_spanned = x_end - x_start + 1 # total number of lines spanned
full_lines_to_insert = lines_spanned - 2
if full_lines_to_insert > 0:
full_lines_to_insert = trunc(full_lines_to_insert * adjusted_ratio)
rle_list.append({'x': (x_start + 1), 'y': 0, 'width': full_lines_to_insert, 'height': image_height})
# Insert bottom line
rle_list.append({'x': x_end, 'y': 0, 'width': 1, 'height': y_end})
if len(rle_list) > 0:
rle_regions[segm['id']] = rle_list
else:
# Add the polygon segmentation
for segmentation_points in segm['segmentation']:
segmentation_points = np.multiply(segmentation_points, adjusted_ratio).astype(int)
polygons_list.append(str(segmentation_points).lstrip('[').rstrip(']'))
polygons[segm['id']] = polygons_list
if i < len(self.colors):
poly_colors[segm['id']] = self.colors[i]
else:
poly_colors[segm['id']] = 'white'
bbox = segm['bbox']
bbox_points = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1],
bbox[0] + bbox[2], bbox[1] + bbox[3], bbox[0], bbox[1] + bbox[3],
bbox[0], bbox[1]]
bbox_points = np.multiply(bbox_points, adjusted_ratio).astype(int)
bbox_polygons[segm['id']] = str(bbox_points).lstrip('[').rstrip(']')
# Print details
print(' {}:{}:{}'.format(segm['id'], poly_colors[segm['id']], self.categories[segm['category_id']]))
# Draw segmentation polygons on image
html = '<div class="container" style="position:relative;">'
html += '<img src="{}" style="position:relative;top:0px;left:0px;width:{}px;">'.format(image_path, adjusted_width)
html += '<div class="svgclass"><svg width="{}" height="{}">'.format(adjusted_width, adjusted_height)
if show_polys:
for seg_id, points_list in polygons.items():
fill_color = poly_colors[seg_id]
stroke_color = poly_colors[seg_id]
for points in points_list:
html += '<polygon points="{}" style="fill:{}; stroke:{}; stroke-width:1; fill-opacity:0.5" />'.format(points, fill_color, stroke_color)
if show_crowds:
for seg_id, rect_list in rle_regions.items():
fill_color = poly_colors[seg_id]
stroke_color = poly_colors[seg_id]
for rect_def in rect_list:
x, y = rect_def['x'], rect_def['y']
w, h = rect_def['width'], rect_def['height']
html += '<rect x="{}" y="{}" width="{}" height="{}" style="fill:{}; stroke:{}; stroke-width:1; fill-opacity:0.5; stroke-opacity:0.5" />'.format(x, y, w, h, fill_color, stroke_color)
if show_bbox:
for seg_id, points in bbox_polygons.items():
fill_color = poly_colors[seg_id]
stroke_color = poly_colors[seg_id]
html += '<polygon points="{}" style="fill:{}; stroke:{}; stroke-width:1; fill-opacity:0" />'.format(points, fill_color, stroke_color)
html += '</svg></div>'
html += '</div>'
html += '<style>'
html += '.svgclass { position:absolute; top:0px; left:0px;}'
html += '</style>'
return html
def process_info(self):
self.info = self.coco['info']
def process_licenses(self):
self.licenses = self.coco['licenses']
def process_categories(self):
self.categories = {}
self.super_categories = {}
for category in self.coco['categories']:
cat_id = category['id']
super_category = category['supercategory']
# Add category to the categories dict
if cat_id not in self.categories:
self.categories[cat_id] = category
else:
print("ERROR: Skipping duplicate category id: {}".format(category))
# Add category to super_categories dict
if super_category not in self.super_categories:
self.super_categories[super_category] = {cat_id} # Create a new set with the category id
else:
self.super_categories[super_category] |= {cat_id} # Add category id to the set
def process_images(self):
self.images = {}
for image in self.coco['images']:
image_id = image['id']
if image_id in self.images:
print("ERROR: Skipping duplicate image id: {}".format(image))
else:
self.images[image_id] = image
def process_segmentations(self):
self.segmentations = {}
for segmentation in self.coco['annotations']:
image_id = segmentation['image_id']
if image_id not in self.segmentations:
self.segmentations[image_id] = []
self.segmentations[image_id].append(segmentation)
Explore Image Meta Data¶
In [3]:
annotation_path = 'IB-SEC.v2i.coco/_annotations.coco.test.json'
filtered_annotation_path = 'IB-SEC.v2i.coco/_annotations.coco.modified.test.json'
image_dir = 'IB-SEC.v2i.coco/test'
coco_dataset = CocoDataset(annotation_path, image_dir)
f_coco_dataset = CocoDataset(filtered_annotation_path, image_dir)
coco_dataset.display_info()
coco_dataset.display_licenses()
coco_dataset.display_categories()
Display sample images - before annotation filtration¶
In [4]:
res = random.sample(range(0, 30), 7)
for img_id in res:
html = coco_dataset.display_image(img_id, use_url=False)
IPython.display.display(IPython.display.HTML(html))
Display sample images - after annotation filtration¶
As seen below, the same images explored above have now only 3 classes (heading, footnote, paragraph) and hence are good to be used for training based on the assessment requirement below:
(Select only 3 classes (headings, paragraphs and footnotes) from the dataset)
In [5]:
for img_id in res:
html = f_coco_dataset.display_image(img_id, use_url=False)
IPython.display.display(IPython.display.HTML(html))